Methods, systems and apparatus to generate market segmentation data with anonymous location data

ABSTRACT

Methods and apparatus are disclosed to determine a consumer classification segment. A disclosed example method involves generating, with a processor, a list of consumer classification segments based on geographic indicators associated with a mobile device, associating, with the processor, the mobile device with a first consumer classification segment of the list of consumer classification segments if the geographic indicators were retrieved while a first application was executed, and associating, with the processor, the mobile device with a second consumer classification segment of the list of consumer classification segments if the geographic indicators were retrieved while a second application was executed.

CROSS REFERENCE TO RELATED APPLICATION

This patent is a continuation of and claims priority to U.S. applicationSer. No. 14/591,205, filed Jan. 7, 2015, entitled “Methods, Systems andApparatus to Generate Market Segmentation Data with Anonymous LocationData,” which is a continuation of and claims priority to U.S.application Ser. No. 13/721,321, filed Dec. 20, 2012, entitled “Methods,Systems and Apparatus to Generate Market Segmentation Data withAnonymous Location Data,” which is a continuation of and claims priorityto U.S. application Ser. No. 12/868,420, filed Aug. 25, 2010, entitled“Methods, Systems and Apparatus to Generate Market Segmentation Datawith Anonymous Location Data,” all of which are hereby incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to advertising, and, moreparticularly, to methods and apparatus to generate market segmentationdata with anonymous location data.

BACKGROUND

In recent years, marketers have collected personal information fromconsumers to aid marketing efforts toward those and similarly situatedconsumers. Consumer personal information typically includes addressinformation, telephone number information and/or zip code information.Such personal information has typically been collected by marketersthrough surveys, promotions and/or retailer programs associated with aretailer that provide a consumer benefit in exchange for consumeraddress information. Retailer programs may include consumer shoppingcards (e.g., “preferred customer cards”) that are barcode scanned at acheckout of the retailer to allow one or more purchased items to bediscounted. Other retailer programs may include merchandise and/or cashincentives based on the amount of purchases made at the retailer storeand/or retailer chain.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example system to generatemarket segmentation data with anonymous location data.

FIG. 2 is a schematic illustration of an example segmentationapplication that may be used in the system of FIG. 1.

FIG. 3 is a schematic illustration of an example segmentation managerthat may be used in the system of FIG. 1.

FIG. 4 is a flowchart representative of example machine readableinstructions that may be executed to implement, for example, the examplesystem shown in FIG. 1.

FIG. 5 is a schematic illustration of an example processor platform thatmay execute the instructions of FIG. 4 to implement any or all of theexample methods, systems and apparatus described herein.

DETAILED DESCRIPTION

Example methods and apparatus are disclosed to generate marketsegmentation data with anonymous location data. A disclosed examplemethod involves receiving geographic location information associatedwith a mobile phone user, associating the geographic locationinformation with an identifier unrelated to personal informationassociated with the mobile phone user, identifying a home locationassociated with the geographic location information, and associatingsegmentation information with the identifier based on the home locationand without referencing personal information of the mobile phone user.

Consumers that provide personal information to a merchant, retailer,and/or marketing entity typically do so in view of a quid-pro-quobenefit. As used herein, merchants, retailers, wholesalers,manufacturers and/or marketing entities will be referred to generally as“marketers.” Benefits expected and/or otherwise received by theconsumers include, but are not limited to, in-store discounts, rewardpoints, cash back, coupons, early purchase opportunities and/or marketerevent information (e.g., e-mail event notifications).

The personal information provided by the consumer may include, but isnot limited to, an address, a home telephone number, a wirelesstelephone number, demographic information, gender, income, occupation,e-mail address, etc. After the consumer provides such information, themarketers may use this information to better tailor advertisements in amanner appreciated and/or otherwise requested by the consumer. In someexamples, knowledge of the consumer's demographic information andoccupation allows the marketers to tailor one or more advertisements tointerests the consumer is likely to have, thereby improving the overalleffectiveness of the advertising campaign(s).

The marketer may employ one or more consumer segmentation service to mapone or more likely market segment types to the consumer based on theconsumer's disclosed home address. An example consumer segmentationsystem includes the Potential Rating Index for Zip Markets (PRIZM) byNielsen®. PRIZM® provides a standardized set of characteristics,referred to as clusters, for each zip code in the United States. In someinstances, PRIZM® provides characteristics for different granularities,such as block groups (e.g., greater than 10 households, but less than azip code), ZIP+4 (e.g., approximately 10 parcels/households) and ZIP+6(e.g., a specific address, sometimes referred to as a delivery pointcode). Block groups and zip+4 information may be digitally stored andreflect a polygon shape when applied to a geographic map. Each zip code(e.g., ZIP, ZIP+4, ZIP+6, block group, etc.) is associated with one ormore of sixty-six (66) demographically and behaviorally distinctsegments. The segment(s) associated with each household providesinformation indicative of likes, dislikes, lifestyles, purchasebehaviors and/or media preferences. As such, marketers may utilizeaddress information with one or more PRIZM® services to reveal anassociated segment most likely associated with the provided addressinformation, thereby allowing the marketer to more accurately tailor oneor more advertisements to the associated consumer at that address.

In other examples, consumers may be reluctant to provide personalinformation, regardless of the quid-pro-quo benefit (e.g., discount,coupon, points, cash-back, etc.) provided by the marketer. Consumerconcerns may include a lack of trust, a belief that the personalinformation provided to the marketer will result in nuisance activity(e.g., unwanted telephone calls (e.g., telemarketing), unwanted mail)and/or a general discomfort with sharing information that the consumerbelieves is personal. In still other examples, consumers may beparticularly reluctant to disclose a wireless (e.g., cellular phones)telephone number for fear that one or more text messaging marketingcampaigns may consume a text message quota of the consumer. As such,marketers may face challenges when attempting to market consumers viatheir wireless telephone.

While wireless smartphones include applications that are cost-free tothe consumer based on advertisements displayed thereon, suchadvertisements are not tailored to specific characteristics of theconsumer. In some examples, the advertisements presented to the consumerare based on their current location determined by global positioningsatellite (GPS) functionality and/or cell tower location (e.g.,triangulation) techniques. Advertisements based on current locationinformation may allow the marketer to tailor advertisements related tomerchants/retailers/wholesalers in the consumer's vicinity. However,such advertising efforts still fail to reflect other characteristics ofthe consumer (e.g., annual income, general preferred vehicle types,etc.) unless that consumer has also relinquished his/her personalinformation.

Example methods and apparatus described herein allow, in part, marketinginformation to be generated based on location information devoid ofpersonal information. The marketing information generated by examplemethods and apparatus described herein is indicative of consumercharacteristics and is generated without requiring the consumer to inputpersonal information. Instead, example methods and apparatus describedherein employ consumer GPS location data aggregated over a period oftime to identify a likely location of the consumer's home/residenceand/or a likely location of the consumer's place of employment. Afterdetermining a home location based on aggregated GPS information, theassociated ZIP, block group, ZIP+4 and/or ZIP+6 is identified andprovided to a market segmentation system (e.g., PRIZM®). The marketsegmentation system (e.g., PRIZM®) returns one or more segments that arelikely associated with the home address.

As described above, PRIZM® includes sixty-six (66) segments indicativeof traits/characteristics of the household member(s) associated with anaddress or location provided by the marketer. For example, the “YoungDigerati” segment describes consumers that are the nation's tech-savvysingles and couples living in fashionable neighborhoods on an urbanfringe. Such consumers are highly educated and ethnically mixed. “YoungDigerati” communities are typically filled with trendy apartments andcondos, fitness clubs, clothing boutiques, casual restaurants, juicebars, coffee bars and microbreweries. When the marketer learns that aconsumer fits within this segment, then that marketer may better tailorone or more advertisements and/or advertising campaigns to improveadvertising efficiency and/or effectiveness.

In some examples, the consumer installs an application on their wirelesstelephone without providing any personal information (e.g., a phonenumber, an address, etc.). In some examples, the application obtains anidentifier, such as an international mobile equipment identity (IMEI)number from the wireless device, performs a hash on the identifier(e.g., the IMEI number) to prevent one or more opportunities topersonally identify the consumer, and then captures GPS data over aperiod of time. The captured GPS data is associated with the hash of theidentifier (e.g., the hash of the IMEI) and, thus, the user may betracked without revealing the identity or identification information ofthe user. Preferably, the identifier does not permit such personalinformation to be derived.

While the GPS data is captured over the period of time, one or morealgorithms may be executed to determine GPS coordinates that are likelyassociated with the user's home, the user's place of work, the user'stravel route(s) and/or the user's leisure location(s). For example, oneor more patterns may be identified based on GPS locations at aparticular time-of-day, GPS locations near industrial areas, GPSlocations near city areas, GPS locations near rural areas, GPS locationsnear residential areas, GPS locations near known tourist areas, etc. Inthe event that a number of GPS location data points are captured duringhours typical of employment (e.g., between the hours of 8:00 AM and 5:00PM during weekdays), then the GPS location data points may be associatedwith a work location. On the other hand, in the event that a number ofGPS location data points are captured during hours associated with restor recreation (e.g., between the hours of 5:00 PM and 8:00 AM), then theGPS location data points may be associated with a home location.Further, in the event that a number of GPS location data points arecaptured that form a path repeated over a number of days during timestypically associated with traveling between employment and recreationsites, then the GPS location data points may be associated with ahome/work traveling route. In still further examples, in the event thata number of GPS location data points are captured for a number ofweekend days in areas known to be vacation destinations, then theassociated GPS location data points may be associated with leisureactivities for the user associated with the hashed IMEI number.

For some examples, an indication of whether a captured GPS location datapoint is to be associated with a home location, a work location, atravel-route location, a leisure activity location, or a vacationlocation may be based on, in part, the type of application that providesthe GPS location data point(s) and/or the type of application(s)executing on the wireless device when the GPS location data point(s) arecaptured. For example, in the event that a GPS navigation application isexecuting on the wireless device, then one or more source locations ordestination locations may be ruled out as home or work under theassumption that both home and work locations are typically known to theuser. Instead, the source and/or destination locations may be associatedwith leisure activities. For other examples, in the event that a stocktrading or financial streaming application is executing on the wirelessdevice when the GPS location data point(s) are captured, then the user'slocation may be deemed to be a work location. In still further examples,in the event that a game application or a movie viewing application isexecuting on the wireless device when the GPS location data point(s) arecaptured, then the user may be deemed traveling, such as by way of car,taxi, train, etc.

Although the aforementioned examples involve an example applicationexecuting on a wireless telephone, the methods and apparatus describedherein may be implemented with GPS location data points acquired fromany other source(s). In some examples, GPS location data points may beacquired by a marketer via a privately maintained customer list, or datapoints collected from GPS devices (e.g., a car-mounted GPS navigationsystem). Preferably, the GPS location data points are separated fromindicators that may reveal personal information associated with theusers. For example, while a wireless telephone company may have abundantdetail related to their customers (e.g., IMEI number, telephone number,home address, social security number, work address, work telephonenumber, etc.), the wireless telephone company is not likely to releaseand/or sell such personal information for profit due to legal privacyobligations and customer goodwill. However, the wireless telephonecompany may sell and/or otherwise provide innocuous identifiers (e.g.,hashed IMEI numbers) having associated GPS location data points that areneither associated with personal customer data nor capable of being usedto derive personal customer data.

After the captured GPS location data points have been analyzed toidentify likely locations associated with a user's home, work, traveland/or leisure locations, such locations are segregated and thecorresponding home location is provided to a segmentation system toidentify a corresponding customer segment. As described above, thePRIZM® methodology may be used to identify one or more of sixty-six (66)customer segments associated with a zip code, a block group (e.g., blockgroups defined by U.S. Census data), a zip+4 (e.g., a 9-digit numberassociated with approximately 40 million U.S. households), a zip+6(e.g., an 11-digit number associated with approximately 120 millionhouseholds). Customer segments may include, but are not limited to“Upper Crust” (e.g., the nation's most exclusive addresses, wealthiestlifestyles), “Beltway Boomers” (e.g., household adult members of thepostwar baby boom, upper-middle-class, home owners), and/or “New EmptyNests” (e.g., households where grown-up children have recently movedout, upscale older Americans that pursue active lifestyles, no interestin rest-home retirement yet over 65-years old).

The one or more likely segments are associated with the home location sothat one or more marketing efforts may yield improved results to thehousehold. For example, in the event that the user uses the applicationon their wireless telephone when in a store (e.g., Best Buy), the hashedIMEI number is used to identify the user's likely segment type. Based onthe likely segment type, the user may be presented with advertisements,coupons and/or promotions that are tailored to that user in view ofsegment characteristics.

FIG. 1 is an illustration of an example system to generate marketsegmentation data based on anonymous location data 100. In theillustrated example of FIG. 1, the system 100 includes a wireless device102 in a geographic area 104 capable of communicating with one or morewireless communication towers 106 a, 106 b, 106 c, 106 d. Thecommunication towers 106 a-d may be, for example, cell phone towers.While the illustrated example of FIG. 1 includes four towers 106 a-d andone wireless device 102, the methods and apparatus described herein mayinclude any number of wireless devices and antenna towers. The examplewireless device 102 may be associated with a wireless provider having acentral office 108 communicatively connected to one or more networks110, such as the Internet. A segmentation manager 112 employs the one ormore networks 110, wireless provider central office 108 and/or towers106 a-d to communicate with a segmentation application 114 executing onthe example wireless device 102.

In operation, the example segmentation application 114 may invoke one ormore functions of the wireless device 102 to capture a GPS location in aperiodic, aperiodic, scheduled or manual manner. In the illustratedexample of FIG. 1, each “X” indicates a GPS location captured by thesegmentation application 114 over a period of time. As described above,location logic is employed to identify a home location, a work locationand/or a leisure location for a user of the wireless device 102. In theillustrated example, a home location 116, a work location 118 and aleisure location 120 have been identified based on, for example,analysis of the wireless device 102 location over a period of time,time-of-day, frequency of location occurrence, known characteristics ofthe adjacent geography (e.g., industrial park, residential subdivision,amusement park, etc.), and/or the type(s) of applications active on thewireless device 102 when the GPS location data was captured.

One or more GPS location data points may be eliminated fromconsideration as the home location 116, the work location 118 or theleisure location 120 when trend analysis and/or filters are notindicative of locations where the user is either at home, at work and/oron vacation. For example, some locations are identified as commutingand/or travel locations based on reoccurring patterns of location. Ahome/work travel route 122 is identified based on a number of GPSlocation data points that occur at a similar time every work day.

FIG. 2 depicts the example segmentation application 114 of FIG. 1 ingreater detail. In the illustrated example of FIG. 2, the segmentationapplication 114 includes a segmentation manager interface 202 tocoordinate communication attempts with the example segmentation manager112, an application monitor 204 to identify which application(s) may beexecuting on the example wireless device 102 at the time a GPS locationdata point is captured, and a GPS interface 206 to invoke locationservices from the wireless device 102. In operation, the segmentationmanager interface 202 may operate on a periodic, aperiodic, scheduledand/or manual basis to capture GPS location data point(s). Thesegmentation manager interface 202 may receive a request from theexample segmentation manager 112 to acquire a GPS location data pointand/or the segmentation application 114 may employ a timer to acquire aGPS location data point upon periodic, aperiodic and/or scheduled times.GPS location data points captured by the example segmentationapplication 114 may be saved to a memory of the wireless device 102and/or may be transmitted to the segmentation manager 112 via one ormore wireless networks (e.g., via a network facilitated by a wirelessservice provider of the wireless device 102, via a WiFi® network incommunication with the wireless device 102, etc.). The GPS data pointscontain time information indicative of the time of capture. This timeinformation may be included in the GPS data or may be added and/orsupplemented with time stamps.

FIG. 3 depicts the example segmentation manager 112 of FIG. 1 in greaterdetail. In the illustrated example of FIG. 3, the segmentation manager112 includes a privacy manager 302, a GPS data analyzer 304, asegmentation application interface 306, a segmentation system 308, and alocation database 310. In operation, the privacy manager 302 receivesone or more identifiers from the example segmentation application 114via the segmentation application interface 306. Identifiers receivedfrom the segmentation application 114 may be implemented by, forexample, an IMEI number, a telephone number, a user identifier, and/orany other identifier that will remain constant throughout the use of thewireless device 102. To maintain privacy for the user of the wirelessdevice 102, the privacy manager 302 applies a one-way hash to thereceived identifier.

GPS location data points received by the segmentation application 114via the segmentation application interface 306 are stored in thelocation database 310 and associated with the hashed value generated bythe privacy manager 302. As such, the user of the wireless device 102 isnever at risk of being identified, yet GPS location data pointscollected from the users are consistently associated with the uniquehash value over time to identify one or more trends, a home location, awork location and/or a leisure location of the user of the wirelessdevice 102. In some examples, the privacy manager 302 may be located on,or otherwise be executed by the mobile device 102, such as within thesegmentation application 114.

The example GPS data analyzer 304 analyzes GPS location data pointsstored in the example location database 310 associated with the hashedidentifier so that locations of interest can be identified, such as ahome location, a work location, a leisure location and/or locationsindicative of travel routes to/from home/work. As described above, logicmay be employed to automatically identify locations of interest. Thelogic may, for example, identify the GPS location data points for agiven identifier over a period of time to identify a threshold number oflocations during a certain time-of-day. For example, GPS location datapoints repeatedly occurring in a first location at 8:00 PM every day maybe indicative of a home location, while GPS location data pointsrepeatedly occurring in a second location at 1:00 PM every day may beindicative of a work location. In still other examples, the GPS dataanalyzer 304 may receive the GPS location data points with an indicationof other applications are executing on the wireless device 102. Suchindications of applications may be used by the GPS data analyzer 304 ashints to determine whether the wireless device 102 is at a homelocation, a work location or one or more locations indicative of travel.In the event that the GPS data analyzer 304 identifies GPS location datapoints repeatedly occurring in a series of locations occurring, forexample, between 8:15 AM and 8:45 AM while a crossword puzzleapplication is being used by the wireless device 102, then the GPS dataanalyzer 304 may identify such series of locations as indicative ofinstances of travel by the user of the wireless device 102.

After one or more locations of interest (e.g., a home location) areassociated with a hashed identifier, the example segmentation system 308matches the home location with marketing segmentation data. While theillustrated example of FIG. 3 includes the segmentation system 308within the segmentation manager 112, it may, instead, be locatedexternal to the segmentation manager 112 and communicatively accessedvia one or more networks 110, such as via the Internet. As describedabove, marketing segmentation data may be provided by the PRIZM® systemor any other marketing segmentation system that provides an indicationof consumer characteristics based on, in part, household location. Forexample, if the home location associated with a hashed identifier ofinterest is located in a segmentation identified as “Upper Crust,” thenone or more marketing efforts directed toward the user associated withthe hashed identifier may be tailored to people generally living awealthy lifestyle, over 55-years old and possessing a postgraduatedegree.

While an example manner of implementing the system to generate marketsegmentation data with anonymous location data 100 of FIG. 1 has beenillustrated in FIGS. 2 and 3, one or more of the elements, processesand/or devices illustrated in FIGS. 1-3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example segmentation application 114, the examplesegmentation manager interface 202, the example application monitor 204,the example GPS interface 206, the example segmentation manager 112, theexample privacy manager 302, the example GPS data analyzer 304, theexample segmentation application interface 306, the example segmentationsystem 308, and/or, more generally, the example location database 310 ofFIGS. 1-3 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example segmentation application 114, the examplesegmentation manager interface 202, the example application monitor 204,the example GPS interface 206, the example segmentation manager 112, theexample privacy manager 302, the example GPS data analyzer 304, theexample segmentation application interface 306, the example segmentationsystem 308, and/or, more generally, the example location database 310could be implemented by one or more circuit(s), programmableprocesssor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), etc. When any of the appended apparatus claims areread to cover a purely software and/or firmware implementation, at leastone of the example segmentation application 114, the examplesegmentation manager interface 202, the example application monitor 204,the example GPS interface 206, the example segmentation manager 112, theexample privacy manager 302, the example GPS data analyzer 304, theexample segmentation application interface 306, the example segmentationsystem 308, and/or, more generally, the example location database 310are hereby expressly defined to include a computer readable medium suchas a memory, DVD, CD, etc. storing the software and/or firmware. Furtherstill, the example segmentation manager 112 and segmentation application114 of FIGS. 1-3 may include one or more elements, processes and/ordevices in addition to, or instead of, those illustrated in FIG. 1-3,and/or may include more than one of any or all of the illustratedelements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the segmentation manager 112 of FIGS. 1 and 3 is shown inFIG. 4. In this example, the machine readable instructions comprise aprogram for execution by a processor such as the processor P105 shown inthe example processor platform (e.g., computer) P100 discussed below inconnection with FIG. 5. The program may be embodied in software storedon a computer readable medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), or a memory associated with theprocessor P105, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor P105and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 4, many other methods of implementing the example segmentationmanager 112 and/or segmentation application 114 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example process of FIG. 4 may be implementedusing coded instructions (e.g., computer readable instructions) storedon a tangible computer readable medium such as a hard disk drive, aflash memory, a read-only memory (ROM), a compact disk (CD), a digitalversatile disk (DVD), a cache, a random-access memory (RAM) and/or anyother storage media in which information is stored for any duration(e.g., for extended time periods, permanently, brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term tangible computer readable medium is expressly definedto include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIG. 4 may be implemented using coded instructions (e.g.,computer readable instructions) stored on a non-transitory computerreadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage media in which informationis stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals.

The program 400 of FIG. 4 begins at block 402 where the examplesegmentation manager 112 invokes the example segmentation application114 to acquire one or more GPS location data points. The examplesegmentation manager interface 202 invokes the GPS interface 206 of thewireless device 102 to acquire the one or more GPS location data points.In some examples, the segmentation application 114 periodically,aperiodically, or on a scheduled basis invokes the GPS interface 206 toacquire one or more GPS location data points. GPS location data pointsmay be stored on the wireless device 102 for a period of time andtransmitted to the example segmentation manager 112 in one or morebatches, or the example segmentation manager interface 202 may transmitthe GPS location data points in response to each acquisition instance.As described above, the methods, apparatus and articles of manufacturedescribed herein may operate without the wireless device 102. Forinstance, some examples include a list of latitude and longitudecoordinates cultivated by a third party and associated with one or moreidentifiers that are devoid of private consumer data. However, theexamples described herein will, for the sake of brevity, include thewireless device 102 as a source of the anonymous latitude and longitudedata points.

The example segmentation application interface 306 receives the one ormore GPS location data points from the segmentation manager interface202, and also receives one or more identifiers from the wireless device102 (block 404). As described above, the one or more identifiers mayinclude, but are not limited to, one or more of an IMEI number, a phonenumber, and/or a user identifier. The example privacy manager 302performs a hash on the identifier received from the wireless device 102and associates the hash with the one or more GPS location data points(block 406) to prevent any personal consumer information from beingdisclosed and/or derived.

To determine which of the one or more received GPS location data pointsare associated with the user's home location, the example GPS dataanalyzer 304 analyzes the GPS location data points to identify one ormore patterns indicative of home (block 408). A home location of theuser of the wireless device 102 may be determined based on, for example,a threshold number of instances where the wireless device 102 is locatedat a first GPS location data point during a particular time of day(e.g., between midnight and 5:00 AM). The home location is associatedwith a corresponding zip code, a zip+4, a zip+6, a block group and/or acentroid (block 410). In some examples, the GPS location data point(s)(e.g., latitude and longitude coordinates) are translated by the examplesegmentation manager 112 to a zip+4 value or a zip+6 value (an address).However, in other examples, a translation to the zip+6 may not bepreferred to maintain user privacy and, instead, a zip+4 value may beused. In the event the GPS location data point(s) do not directlytranslate to a zip+4, the segmentation manager 112 may select a centroidof a polygon of addresses closest to a zip+4 value of the polygon. Ifthe corresponding zip code, zip+4, zip+6, block group and/or centroid isindicative of a residential area and/or residential address (block 412),then the example segmentation system 308 associates the home locationwith a corresponding population segmentation type (e.g., one of 66 typesdescribed above) (block 414).

One or more advertisements, marketing promotions and/or other marketingefforts are directed to the hashed identifier in a manner tailored basedon the corresponding population segmentation type (block 416). Forexample, the example segmentation application 114 may broadcast thehashed identifier from the wireless device 102 when the user enters aretail establishment (e.g., Best Buy). The retail establishment mayinvoke a advertising system that, upon receiving the anonymous hashedidentifier, pushes one or more advertisements, coupons and/or othermarketing information to the user of the wireless device 102 in a mannertailored to their corresponding population segmentation type, therebyimproving the effectiveness of the advertising attempt(s).

FIG. 5 is a schematic diagram of an example processor platform P100 thatmay be used and/or programmed to implement the instructions of FIG. 4and any or all of the example segmentation application 114, the examplesegmentation manager interface 202, the example application monitor 204,the example GPS interface 206, the example segmentation manager 112, theexample privacy manager 302, the example GPS data analyzer 304, theexample segmentation application interface 306, the example segmentationsystem 308, and/or the example location database 310 of FIGS. 1-3. Forexample, the processor platform P100 can be implemented by one or moregeneral-purpose processors, processor cores, microcontrollers, etc. Theprocessor platform P100 can be, for example, a server, a personalcomputer, a mobile phone (e.g., a cell phone), an Internet appliance, orany other type of computing device.

The processor platform P100 of the example of FIG. 5 includes at leastone general-purpose programmable processor P105. The processor P105 canbe implemented by one or more Intel® microprocessors from the Pentium®family, the Itanium® family or the XScale® family. Of course, otherprocessors from other families are also appropriate. The processor P105executes coded instructions P110 and/or P112 present in main memory ofthe processor P100 (for example, within a RAM P115 and/or a ROM P120).The coded instructions may be, for example, the instructions implementedby FIG. 4. The processor P105 may be any type of processing unit, suchas a processor core, a processor and/or a microcontroller. The processorP105 may execute, among other things, the example process of FIG. 4 toimplement the example methods and apparatus described herein.

The processor P105 is in communication with the main memory (including aROM P120 and/or the RAM P115) via a bus P125. The RAM P115 may beimplemented by dynamic random access memory (DRAM), synchronous dynamicrandom access memory (SDRAM), and/or any other type of RAM device, andROM may be implemented by flash memory and/or any other desired type ofmemory device. Access to the memory P115 and the memory P120 may becontrolled by a memory controller (not shown).

The processor platform P100 also includes an interface circuit P130. Theinterface circuit P130 may be implemented by any type of interfacestandard, such as an external memory interface, serial port,general-purpose input/output, etc. One or more input devices P135 andone or more output devices P140 are connected to the interface circuitP130. The interface circuit P130 can be implemented by, for example, akeyboard, a mouse, a touchscreen, a track-pad, a trackball, isopointand/or a voice recognition system. The output devices P140 can beimplemented, for example, by display devices (e.g., a liquid crystaldisplay, a cathode ray tube display (CRT), a light-emitting-diode (LED)display, a printer and/or speakers).

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture permit consumersegmentation type identification without the use of consumer personalidentification information and without such personal identificationinformation being disclosed and/or derived, thereby maintaining consumerprivacy.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1-10. (canceled)
 11. A computer-implemented method to deliver tailored advertisements to a mobile device based on a consumer classification segment comprising: associating, by executing an instruction with a processor, a mobile device with an anonymous identifier to protect privacy of a user of the mobile device; determining, by executing an instruction with the processor, a first location of interest based on (a) a pattern of location data collected from the mobile device and (b) an application type of an application executing on the mobile device when the location data are collected; associating, by executing an instruction with the processor, the anonymous identifier with the consumer classification segment based on the first location of interest; and delivering, by executing an instruction with the processor, one of the tailored advertisements to the mobile device based on the consumer classification segment.
 12. (canceled)
 13. The method as defined in claim 11, further including applying a one-way hash to an identifier of the mobile device to produce the anonymous identifier.
 14. (canceled)
 15. The method as defined in claim 11, further including translating the first location of interest to form an alternate location of interest associated with the mobile device.
 16. (canceled)
 17. (canceled)
 18. The method as defined in claim 11, further including determining a second location of interest, the second location of interest associated with a retailer, and delivering the one of the tailored advertisements associated with the retailer located at the second location of interest.
 19. The method as defined in claim 11, further including identifying the pattern of location data based on a threshold number of location data points corresponding to a same location captured at a same time of day.
 20. The method as defined in claim 11, further including eliminating a location from consideration as the first location of interest based on the application type.
 21. A tangible computer readable storage medium comprising computer readable instructions that, when executed, cause a processor to, at least: associate a mobile device with an anonymous identifier to protect privacy of a user of the mobile device; determine a first location of interest based on (a) a pattern of location data collected from the mobile device and (b) an application type of an application executing on the mobile device when the location data are collected; associate the anonymous identifier with the consumer classification segment based on the first location of interest; and deliver a tailored advertisement to the mobile device based on the consumer classification segment.
 22. (canceled)
 23. The computer readable medium as defined in claim 21, wherein the instructions, when executed, cause the processor to apply a one-way hash to an identifier of the mobile device to produce the anonymous identifier.
 24. (canceled)
 25. The computer readable medium as defined in claim 21, wherein the instructions, when executed, cause the processor to translate the first location of interest to form an alternate location of interest associated with the mobile device.
 26. (canceled)
 27. The computer readable medium as defined in claim 25, wherein the instructions, when executed, cause the processor to calculate a centroid location based on addresses within the pattern of location data to translate the first location of interest to form the alternate location of interest associated with the mobile device.
 28. The computer readable medium as defined in claim 21, wherein the instructions, when executed, cause the processor to determine a second location of interest, the second location of interest associated with a retailer, and to deliver the one of the tailored advertisements associated with the retailer located at the second location of interest.
 29. The computer readable medium as defined in claim 21, wherein the instructions, when executed, cause the processor to identify the pattern of location data based on a threshold number of location data points corresponding to a same location captured at a same time of day.
 30. The computer readable medium as defined in claim 21, wherein the instructions, when executed, cause the processor to eliminate a location from consideration as the first location of interest based on the application type.
 31. A system to deliver tailored advertisements to a mobile device based on a consumer classification segment comprising: means for associating the mobile device with an anonymous identifier to protect privacy of a user of the mobile device; means for determining a first location of interest based on (a) a pattern of location data collected from the mobile device and (b) an application type of an application executing on the mobile device when the location data are collected; means for associating the anonymous identifier with the consumer classification segment based on the first location of interest; and means for delivering one of the tailored advertisements to the mobile device based on the consumer classification segment.
 32. (canceled)
 33. The system as defined in claim 31, further including means for applying a one-way hash to an identifier of the mobile device to produce the anonymous identifier.
 34. (canceled)
 35. The system as defined in claim 31, further including means for translating the first location of interest to form an alternate location of interest associated with the mobile device.
 36. (canceled)
 37. The system as defined in claim 35, further including means for calculating a centroid location based on addresses within the pattern of location data to translate the first location of interest to form the alternate location of interest associated with the mobile device.
 38. The system as defined in claim 31, further including means for determining a second location of interest, the second location of interest associated with a retailer, and means for delivering the one of the tailored advertisements associated with the retailer located at the second location of interest.
 39. The system as defined in claim 31, further including means for identifying the pattern of location data based on a threshold number of location data points corresponding to a same location captured at a same time of day.
 40. The system as defined in claim 31, further including means for eliminating a location from consideration as the first location of interest based on the application type. 